Sea Surface Research Articles

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.

Role of the sea surface biofilm in regulating the Earth’s climate

Role of the sea surface biofilm in regulating the Earth’s climate

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Distinct responses of tropical cyclone activity to spatio-uniform and nonuniform SST warming patterns

Most previous studies have reported a decrease in global tropical cyclone (TC) genesis frequency (TCGF) under anthropogenic warming. However, little attention has been drawn to the influence of sea surface temperature (SST) warming patterns on TCGF changes. Here, we investigate the impacts of three distinct SST warming patterns on global TCGF: uniform SST warming, nonuniform (El Niño-like) SST warming, and a combination of both. Results show that spatio-uniform SST warming has a limited impact on global TCGF, instead redistributing the TC genesis locations. Conversely, nonuniform SST warming significantly suppresses global TCGF. The combined warming produces a similar decrease in TCGF to nonuniform warming albeit with differences in spatial distribution. This indicates the dominant role of nonuniform SST warming in affecting TCGF and highlights the nonlinearity of the process. Further analysis shows that these differences in TCGF primarily stem from the distinct responses of tropical circulations to the three warming patterns.

Passive millimeter wave imaging low altitude detection technology

Passive millimeter wave imaging refers to the passive detection of naturally occurring background millimeter waves. After receiving external millimeter wave thermal radiation signals, the passive millimeter wave detection system will form images based on temperature differences and detect targets. Passive millimeter wave imaging has the advantages of non-radiation, non-contact, perspective imaging, good concealment, small size, and low power consumption. It is widely used in safety inspections, aircraft landing, low visibility navigation, sea surface detection and other fields. Driven by high and new technology, passive millimeter wave imaging low-altitude detection technology is rapidly developing. This paper introduced the basic principle of passive millimeter wave imaging, including the characteristics of millimeter wave radiation and Black-body radiation law, as well as its comparison with ordinary optical radiation. It also described the basic model of current passive millimeter wave imaging low altitude detection technology, explored the shortcomings of this technology and pointed out further work as well.

Improving the modeled variability estimates of offshore winds in Northern Europe by nudging ASCAT-derived winds

Abstract The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind Lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the one-year-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples.

Variational Reconstruction and Simulation Experiments of Sea Surface Wind Field for Ocean Data Buoy

Variational Reconstruction and Simulation Experiments of Sea Surface Wind Field for Ocean Data Buoy

Why Does Extreme Precipitation Occur in Borneo During Both El Niño and La Niña Winters?

AbstractExtreme precipitation over the Maritime Continent (MC) is not only vital for local livelihoods, but also influences atmospheric circulation in tropical and extra‐tropical regions. The warm (cold) phase of El Niño‐Southern Oscillation usually leads to dry (wet) condition around the MC in boreal winter. This study unravels that extreme precipitation occurs in Borneo during both El Niño and La Niña winters. Extreme precipitation in Borneo is associated with tripole and dipole anomalous vertical motion pattern, respectively, over the MC‐tropical Pacific in El Niño and La Niña years. A parallel analysis reveals that weak precipitation in Borneo in El Niño and La Niña years occurs with dipole and tripole anomalous vertical motion pattern, respectively, over the MC‐tropical Pacific. The amplitude of the equatorial central‐eastern Pacific sea surface temperature (SST) anomalies may be a factor in the change of anomalous vertical motion pattern over the MC‐tropical Pacific. Further analysis identifies a tendency of extreme precipitation in late December to early−mid January and weak precipitation in February in Borneo. This suggests a critical role of seasonal change in the eastern equatorial Pacific SST in the occurrence of extreme precipitation in Borneo.

Experimental and numerical investigation of shelf flow crossing over a strait

AbstractMotivated by the phenomenon of Scotian Shelf Crossover events, the problem of a shelf flow that is interrupted by a strait is considered. Laboratory experiments in a rotating tank with barotropic and baroclinic flow over flat and sloping shelves confirm that the flow is steered by the bathymetric contours and mainly circumnavigates the gulf. In order to jump across the strait, as suggested by earlier theories, the flow must have unrealistically high Rossby numbers. However, the near bottom friction relaxes the bathymetric constraint and causes the formation of a peculiar jet crossing the strait diagonally. For the dissipation values such that a half of the transport goes around the gulf and half crosses the strait diagonally, the diagonal crossover jet becomes most evident. Numerical solutions for realistic values of the frictional parameter reproduce the results of the laboratory experiments and consideration of the actual Gulf of Maine bathymetry reproduces patterns similar to those observed by drift trajectories and in the satellite derived sea surface temperature fields.

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

Asymmetry of hemispheric climate during MIS 16 versus MIS 12

Marine isotope stage (MIS) 16 (676-621 ka) and MIS 12 (478-424 ka) stand out as two significant glacial periods during the late Quaternary, demonstrating consistent continental ice volume or sea level, as discerned from benthic δ18O records. Intriguingly, the average sea surface temperature (SST) during MIS 16 surpasses that of MIS 12 by approximately 0.54–0.79 °C, while the atmospheric CO2 concentration during MIS 16 is approximately 16 ppmv lower than that of MIS 12. The underlying mechanism driving these discrepancies remains enigmatic. In this study, we present new high-resolution X-ray fluorescence (XRF) and grain size records covering the interval from 700 to 400 ka from Ocean Drilling Program (ODP) Site 1090 in the South Atlantic Ocean. We also integrated benthic δ13C, ice-rafted debris (IRD) and carbonate ion concentration ([CO32−]) records globally, to elucidate the distinct climate evolution during the MIS 16 versus MIS 12. Our findings suggest that MIS 12 is distinguished by a more substantial Antarctic ice-sheet, coupled with a weakened and/or equatorward displacement of southern westerly winds, and expansion of southern source water (SSW) alongside contractive northern source water (NSW). Conversely, the opposite climate state can be observed in MIS 16. The elevated atmospheric CO2 in MIS 12 and diminished atmospheric CO2 in MIS 16 may be attributed to reconfigurations in the ocean carbon reservoir regulated by the reorganization of deep-water masses and meridional ocean circulation, as a result of the divergent evolution of bipolar ice-sheets. Our study underscores the critical role of the interplay between the two polar regions in modulating deep-sea carbon storage on the orbital timescale and atmospheric CO2. This interplay is essential for a comprehensive understanding of past and future carbon cycle adjustments in response to climate change.

Revealing Spatial–Temporal Patterns of Sea Surface Temperature in the South China Sea Based on Spatial–Temporal Co-Clustering

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward.

Impact of assimilation of absolute dynamic topography on Arctic Ocean circulation

The ocean circulation is typically constrained in operational analysis and forecasting systems through the assimilation of sea level anomaly (SLA) retrievals from satellite altimetry. This approach has limited benefits in the Arctic Ocean and surrounding seas due to data gaps caused by sea ice coverage. Moreover, assimilation of SLA in seasonally ice-free regions may be negatively affected by the quality of the Mean Sea Surface (MSS) used to derive the SLA. Here, we use the Regional Ice Ocean Prediction System (RIOPS) to investigate the impact of assimilating Absolute Dynamic Topography (ADT) fields on the circulation in the Arctic Ocean. This approach avoids the use of a MSS and additionally provides information on sea level in ice covered regions using measurements across leads (openings) in the sea ice. RIOPS uses a coupled ice-ocean model on a 3-4 km grid-resolution pan-Arctic domain together with a multi-variate reduced-order Kalman Filter. The system assimilates satellite altimetry and sea surface temperature together with in situ profile observations. The background error is modified to match the spectral characteristics of the ADT fields, which contain less energy at small scales than traditional SLA due to filtering applied to reduce noise originating in the geoid product used. A series of four-year reanalyses demonstrate significant reductions in innovation statistics with important impacts across the Arctic Ocean. Results suggest that the assimilation of ADT can improve circulation and sea ice drift in the Arctic Ocean, and intensify volume transports through key Arctic gateways and resulting exchanges with the Atlantic Ocean. A reanalysis with a modified Mean Dynamic Topography (MDT) is able to reproduce many of the benefits of the ADT but does not capture the enhanced transports. Assimilation of SLA observations from leads in the sea ice appears to degrade several circulation features; however, these results may be sensitive to errors in MDT. This study highlights the large uncertainties that exist in present operational ocean forecasting systems for the Arctic Ocean due to the relative paucity and reduced quality of observations compared to ice-free areas of the Global Ocean. Moreover, this underscores the need for dedicated and focused efforts to address this critical gap in the Global Ocean Observing System.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Vegetation Changes and Dynamics of the Climate Variables in Southern Thailand over the Past 1500 Years

The Indo-Pacific, a vast biogeographic of Earth, is influenced by both the Indian and East Asian monsoons. Despite its geographical importance, this region has been less studied compared to East Asia and India. Here, we present speleothem records from southern Thailand that cover the last 1500 years, including a hiatus during the Little Ice Age, providing insights into the interactions among climate dynamics, human influences, and ecological responses to climate change. Notably, our records lack the characteristic cold and warm periods observed in other regions, such as the Dark Ages Cold Period and Medieval Warm Period, which may reflect the complexity of the tropical climate system or the region’s unique topography. The analysis reveals a link between ENSO multi-decadal variability and hydroclimate conditions in southern Thailand, as evidenced by speleothem δ18O. Furthermore, a comparison between speleothem δ13C and the Normalized Difference Vegetation Index (NDVI) indicates significant vegetation changes in the last three decades, corresponding with increased atmospheric CO2 levels and expansion of agricultural land due to human activities during the Current Warm Period. Additionally, our study suggests that an abrupt increase in sea surface temperatures may enhance vegetation growth in the Indo-Pacific by influencing atmospheric circulation and increasing precipitation.

Interactions between trade wind clouds and local forcings over the Great Barrier Reef: a case study using convection-permitting simulations

Abstract. Trade wind clouds are ubiquitous across the subtropical oceans, including the Great Barrier Reef (GBR), playing an important role in modulating the regional energy budget. These shallow clouds, however, are by their nature sensitive to perturbations in both their thermodynamic environment and microphysical background. In this study, we employ the Weather Research and Forecasting (WRF) model with a convection-permitting configuration at 1 km resolution to examine the sensitivity of the trade wind clouds to different local forcings over the GBR. A range of local forcings including coastal topography, sea surface temperature (SST), and local aerosol loading is examined. This study shows a strong response of cloud fraction and accumulated precipitation to orographic forcing both over the mountains and upwind over the GBR. Orographic lifting, low-level convergence, and lower troposphere stability are found to be crucial in explaining the cloud and precipitation features over the coastal mountains downwind of the GBR. However, clouds over the upwind ocean are more strongly constrained by the trade wind inversion, whose properties are, in part, regulated by the coastal topography. On the scales considered in this study, the warm-cloud fraction and the ensuant precipitation over the GBR show only a small response to the local SST forcing, with this response being tied to the surface flux and lower troposphere stability. Cloud microphysical properties, including cloud droplet number concentration, liquid water path, and precipitation, are sensitive to the changes in atmospheric aerosol population over the GBR. While cloud fraction shows little responses, a slight deepening of the simulated clouds is evident over the upwind region in correspondence to the increased aerosol number concentration. A downwind effect of aerosol loading on simulated cloud and precipitation properties is further noted.

The role of atmospheric CO2 in controlling sea surface temperature change during the Pliocene

Abstract. We present the role of CO2 forcing in controlling Late Pliocene sea surface temperature (SST) change using six models from Phase 2 of the Pliocene Model Intercomparison Project (PlioMIP2) and palaeoclimate proxy data from the PlioVAR working group. At a global scale, SST change in the Late Pliocene relative to the pre-industrial is predominantly driven by CO2 forcing in the low and mid-latitudes and non-CO2 forcing in the high latitudes. We find that CO2 is the dominant driver of SST change at the vast majority of proxy data sites assessed (17 out of 19), but the relative dominance of this forcing varies between all proxy sites, with CO2 forcing accounting for between 27 % and 82 % of the total change seen. The dearth of proxy data sites in the high latitudes means that only two sites assessed here are predominantly forced by non-CO2 forcing (such as changes to ice sheets and orography), both of which are in the North Atlantic Ocean. We extend the analysis to show the seasonal patterns of SST change and its drivers at a global scale and at a site-specific level for three chosen proxy data sites. We also present a new estimate of Late Pliocene climate sensitivity using site-specific proxy data values. This is the first assessment of site-specific drivers of SST change in the Late Pliocene and highlights the strengths of using palaeoclimate proxy data alongside model outputs to further develop our understanding of the Late Pliocene. We use the best available proxy and model data, but the sample sizes remain limited, and the confidence in our results would be improved with greater data availability.

Wide range of possible trajectories of North Atlantic climate in a warming world.

Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

ST-ADPTC: a method for clustering spatiotemporal raster data based on improved density peak detection

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

IntroductionSound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.MethodIn this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.ResultIt can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.DiscussionThis enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.